I'm not sure how to start on either method with 1)
For 2) doing the RRE form method, http://latex.codecogs.com/gif.latex?A can be reduced down to a matrix with no nonzero rows, but I get stuck there.
I'm confused about 3, if http://latex.codecogs.com/gif.latex?A has rank does that mean that ?
4) I understand the RRE form method because it can be reduced to the identity matrix, but I don't know how to prove it using the rank nullity formula

Any help would be very much appreciated!

Jan 4th 2013, 09:42 AM

GJA

Re: Prove number of solutions to Ax=b two ways: rank nullity formula and RRE form and

Hi Rachel123,

For 1) suppose is a solution. Now looking at the rank-nullity theorem might be helpful. Since what can we conclude about the kernel of

I think once the answer to this question is established you'll see what to do. If this is too cryptic let me know and I'll give further hints. Good luck!

Jan 4th 2013, 09:53 AM

Deveno

Re: Prove number of solutions to Ax=b two ways: rank nullity formula and RRE form and

1) this is the same as saying: if Ax = b has even one solution, it has infinitely many (one and only one is not an option).

suppose that m < n. the rank-nullity theorem tells us that:

n = rank(A) + dim(ker(A))

since rank(A) = dim(im(T)) ≤ m < n, this tells us that dim(ker(A)) ≥ 1. in particular, there must be some non-zero vector v in the nullspace of A.

it then stands to reason that we have an infinite number of vectors in the nullspace of A, av, for every non-zero real number a:

A(av) = a(Av) = a(0) = 0 (with 0 meaning the 0-vector of Rm).

if we have one vector x with Ax = b, then we have the infinite number of vectors {x + av: v in ker(A), a in R*}.

0 = av, contradicting that a is a non-zero real number, and v is a non-zero vector.

for 2) suppose that A is of rank m. doesn't this mean that im(T) is all of Rm (that is, that T is onto)?

for 3) yes, for rank(A) = n, m has to be "big enough" for Rm to fit an n-dimensional subspace (im(T)) inside it. what must the nullspace of A be, in this case?

for 4) in this case, we have: n = rank(A) + dim(ker(A)) = n + dim(ker(A)). what does dim(ker(A)) = 0 tell us about how big the nullspace is?

can we use (2) and (3) in some special way here?

Jan 4th 2013, 10:20 AM

Rachel123

Re: Prove number of solutions to Ax=b two ways: rank nullity formula and RRE form and

Thanks for the explanation of 1). 2) seems pretty clear too now
For 3) the nullspace of A consists of just the empty set, right? So there does not exist and vector v such that Av=0 and then it's just the reverse argument of A. Hence, if there does exist a solution, it is the only one?
Oh, and then 4 can be shown using logic from 2) and 3)
Thank you!

And I've figured out the reduced row echelon form methods too now, so thanks! :)

Jan 4th 2013, 01:03 PM

Deveno

Re: Prove number of solutions to Ax=b two ways: rank nullity formula and RRE form and

no the BASIS of the nullspace is the empty set. the nullspace itself is not empty it contains the 0-vector (trivially, A(0) = 0, for any matrix A, or indeed for any linear transformation T). remember, the nullspace of A IS a subspace of Rn and subspaces can't be empty.

now suppose we want to use RREF instead. let's look at that.

1) we start with a matrix A, and put it in RREF by using ERO. since A only has m rows, it can at most have rank m or less (m non-zero rows in RREF).

the matrices we use to put A in RREF are all invertible, so we wind up with a matrix A' = PA (for some invertible matrix P representing the accumulated ERO).

note that if Ax = b has a solution, let's call it x0, so does A'x = b' (where b' is the b with the same ERO done to IT, that is Pb):

A'x0 = PAx0 = Pb (usually A|b are combined as an AUGMENTED matrix, and put in RREF form to get an augmented matrix A'|b'), that is every solution to Ax = b is also a solution to A'x = b' (that is PAx = Pb).

the non-zero rows of A' (= RREF(A)) all start with a leading 1. we have rank(A) ≤ m of these, so at most m columns in which these leading 1's appear (these are called "pivot columns").

let's look at what our matrix equation looks like:

let's suppose Ax = b has some solution, so that this has some solution, too. what is going to be of interest to us are the solutions to:

A'x = 0. note we now have k ≤ m < n equations in n unknowns (one for each non-zero row of A'). look at the last non-zero row of A', which gives us an equation for the k-th entry of A'x (which will be 0):

(the first few akj will be 0, and the first non-zero akj will be 1).

this tells us that for some j:

. this tells us no matter how we choose the last n-j coordinates for x, that determines our choice for xj.

going up to the row above, we have:

for some j' < j.

this means no matter how we choose the last n-j' coordinates for x, this determines our choice of xj'.

so pick the last n-j coordinates of x arbitrarily, which will determine xj, then pick the next j-j'-1 coordinates arbitrarily, which will then determine xj'